class_docstr: Gated Graph Convolution layer from paper `Gated Graph Sequence
  Neural Networks <https://arxiv.org/pdf/1511.05493.pdf>`__.
  
  .. math::
     h_{i}^{0} = [ x_i \| \mathbf{0} ]
  
     a_{i}^{t} = \sum_{j\in\mathcal{N}(i)} W_{e_{ij}} h_{j}^{t}
  
     h_{i}^{t+1} = \mathrm{GRU}(a_{i}^{t}, h_{i}^{t})
  
  Parameters
  ----------
  input_size: int
      Input feature size.
  output_size: int
      Output feature size.
  num_layers: int
      Number of GGNN layers. Default: 1.
  n_etypes: int
      Number of edge types. Default: 1.
  bias: bool
      If True, adds a learnable bias to the output. Default: True.
docstr: Reinitialize learnable parameters.
docstr_tok: ["Reinitialize","learnable","parameters","."]
code: def reset_parameters(self):
      """Reinitialize learnable parameters."""
      gain = init.calculate_gain("relu")
      self.gru.reset_parameters()
      for linear in self.linears:
          init.xavier_normal_(linear.weight, gain=gain)
          init.zeros_(linear.bias)
  
imports: import dgl
  import dgl.function
  import torch
  import torch.nn
  from torch.nn import init
  from ...data.data import GraphData
  from .base import GNNBase, GNNLayerBase
label_desc: Use image node_id 2 for calling the UndirectedGGNNLayerConv obj's underlying member method code with example usage: obj.reset_parameters() without return types